Realistic barrel cortical column - NetPyNE (Huang et al., 2022)

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Accession:267551
Reconstructed rodent barrel cortical column (thalamic filter-and-fire input, L4 and L2/3 spiking neurons) based on measured distributions, so each run will create a different connectivity). Includes 13 types of inhibitory and excitatory neurons, implemented as Izhikevich neurons. Includes both a Matlab and a Python (NetPyNe) implementation.
Reference:
1 . Huang C, Zeldenrust F, Celikel T (2022) Cortical Representation of Touch in Silico Neuroinformatics [PubMed]
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Model Information (Click on a link to find other models with that property)
Model Type: Realistic Network; Spiking neural network;
Brain Region(s)/Organism: Barrel cortex;
Cell Type(s): Abstract Izhikevich neuron; Barrel cortex L2/3 pyramidal cell;
Channel(s):
Gap Junctions:
Receptor(s):
Gene(s):
Transmitter(s):
Simulation Environment: NetPyNE;
Model Concept(s): Long-term Synaptic Plasticity; Action Potentials; Synaptic Integration; Synaptic Plasticity; Calcium dynamics; Sensory coding; Spike Frequency Adaptation; Spatial connectivity;
Implementer(s): Zeldenrust, Fleur [fleurzeldenrust at gmail.com]; Huang, Chao; Celikel, T;
# Cortical-representation-of-touch-in-silico (NetPyNE version)

Biologically inspired, computationally efficient network model of the somatosensory cortex after reconstructing the mouse barrel cortex in soma resolution and defining a mathematical model of cortical neurons whose action potential threshold adapts to the rate of ongoing network activity impinging onto the postsynaptic neuron.

This is a NetPyNE implementation, the original model, written in Matlab, can be found [here](https://github.com/DepartmentofNeurophysiology/Cortical-representation-of-touch-in-silico).

A detailed explanation of the NetPyNE implementation can be found [in the folder "Documentation"](https://github.com/DepartmentofNeurophysiology/Cortical-representation-of-touch-in-silico-NetPyne/blob/main/Documentation/Cortical%20representation%20of%20touch%20in%20silico.docx).